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// WORKSHOP // EA Modeling using C4 and ArchiMate

In English.

Like the saying “A picture is worth a thousand words”, Inera found that “An Enterprise Architecture model says more than a thousand Excel matrices”. To express an Enterprise Architecture (EA), the most crucial decisions are to select a suitable modeling-language and -tool. We discovered that the ArchiMate language and the tool Archi met all our functional and non-functional requirements, completely for free. When it came to expressing the software architecture aspect of our software systems, we had a bit of a struggle. ArchiMate is a bit too flexible and complex for us architects to use in a consistent way, which became confusing for our non-technical users. C4 came to the rescue, it’s not as flexible and complex and way more intuitive. And since it’s designed to be notation- and tooling-independent, we could easily introduce it into our model.

In this workshop, we will teach you how to express software architecture with C4 notation in ArchiMate. And how to relate it to other aspects of the EA. We will begin with a presentation and end with practical work in the Archi modeling tool.

Bring your own device for the practical work and, if you already have Archi installed, you will get a flying start. (https://www.archimatetool.com/)

AI in the service of democracy

The Swedish Security Service has been scaling up the data and AI efforts in recent years, but why and how can AI be used by the service? Many organizations want to scale their digitalization and AI capabilities, but how can these capabilities ”scale with style?” AI has been around for over 70 years, but why is it accelerating at an exponential rate today? How are investments in AI being made in different parts of the World, and how do the Swedish and European investments compare? These are some of the questions that will be addressed in this talk, in the context of using AI in the service of democracy.

The Rise and Fall and Rise of Artificial Intelligence

Speaking remote

It is perhaps in the very nature of our humanity that we seek to build machines in our own image. The history of computing is filled with attempts to do so, and even today, fueled by a perfect storm of an abundance of data, access to extraordinary amounts of computational power, and advances in neural algorithms, we see such efforts renewed. In this presentation, I’ll look at AI through the lens of software architecture: how it has evolved, what has worked and what has not, and what remains to be done.

Barry O'Reilly

Architecture as Difference and Repetition

It turns out that the never-ending discussion on ”What is architecture?” is almost 3000 years old. This session will use aspects of 20th century continental philosophy to explain why traditional architectural approaches should never work, why they sometimes do despite themselves, and what we should be doing instead.

Data Science as Software Engineering

Data science has been booming! ChatGPT fills your feeds and there are self-proclaimed ’AI experts’ everywhere. But does data science actually add value for companies?

In this talk we’ll take a look at how we got to this point, share experiences of implementing data science projects at companies and discuss how software engineering will play a key role in its future.

Speaker-itarc

The Lost Art of Software Modelling

”Big design up front is dumb. Doing no design up front is even dumber.” This quote epitomises what I’ve seen during our journey from ”big design up front” in the 20th century, to ”emergent design” and ”evolutionary architecture” in the 21st. In their desire to become ”agile”, many teams seem to have abandoned architectural thinking, up front design, documentation, diagramming, and modelling. In many cases this is a knee-jerk reaction to the heavy bloated processes of times past, and in others it’s a misinterpretation and misapplication of the agile manifesto.

As a result, many of the software design activities I witness these days are very high-level and superficial in nature. The resulting output, typically an ad hoc sketch on a whiteboard, is usually ambiguous and open to interpretation, leading to a situation where the underlying solution can’t be communicated, assessed, or reviewed. The same is true of long-lived documentation, which is typically a collection of disconnected diagrams that are out of sync, and out of date. Modelling can help resolve many of these problems, but that’s a tough thing to sell to mainstream developer audiences these days – teams are either not aware of modelling, or they associate it with bad experiences using complicated CASE tools from the past. Join me for a discussion about the lost art of software architecture modelling, and my experiences of how it can be reintroduced to the agile generation.

Order and Emergence: Self-Organisation in Nature

No one can predict the future, least of all software engineers. As our society bears witness to an age of increasing complexity, how does one even begin to judge the validity of today’s KPI/OKR/KRA/TLA against the backdrop of the unpredictable?

Mother Nature takes a simpler approach: do what you can and try not to die. While the rest of the cosmos marches down the inevitable path towards maximum disorder, Nature seemingly never stops creating expansive islands of ever-increasing order. By exploring the boundaries of mathematics and chaos, structure and self-organisation can be found everywhere from economics and social networks, to chemistry and biology. But how is this possible?

This talk highlights several key ideas that have emerged over the past century, and tells the story of how Nature evolves, reacts and experiments to create amazing structures, limited only by the universe itself. On our journey, we will explore the awesome power of weather systems, the elegance of the human brain, the unfair reputation of waterfalls, and the reasons why pandas are pointless.

Modelling and Reality

To make sense of the world, we rely on our brains’ capability to form fictions that we call ”categories” of things and experiences. This capability is both automatic and hidden: we can’t avoid doing it, yet we don’t know exactly how we do it. We know that differences and similarities play a role, but how? When we try to be more deliberate about the process, for instance because we want to write software based on our categories, we call it modelling. In the process, we tend to replace our intuitive but ill-defined common-sense categories with more precise technical categories. But precision comes at a cost. In this talk, we’ll look at different perspectives on categorization, see that nothing remains the same for long, and that edge cases are just regular cases that got unlucky.